mbs-octoml commented on code in PR #11474: URL: https://github.com/apache/tvm/pull/11474#discussion_r887407270
########## tests/python/relay/transform/test_compiler_function_utils.py: ########## @@ -0,0 +1,162 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License +"""Unit tests for the OutlineCompilerFunctionsWithExistingGlobalSymbols and + MarkCompilerFunctionsAsExtern external codegen helper passes.""" + +import tvm +import tvm.testing +import numpy as np + + +def make_const(dtype, shape): + return tvm.relay.const(np.random.rand(*shape).astype(dtype)) + + +def make_consts(dtype, shapes): + return [make_const(dtype, shape) for shape in shapes] + + +metatable = { + "relay.Constant": make_consts( + "float16", + [ + (2304, 768), # 0 + (2304,), # 1 + (600, 32, 64), # 2 + ], + ), + "attributes": [{"relay_attrs": None}], +} + + +def inlined_mod(): + return tvm.parser.parse( + """ + #[version = "0.0.5"] + def @main(%x0 : Tensor[(1600, 768), float16], %x3 : Tensor[(600, 32, 64), float16]) -> (Tensor[(1600, 2304), float16], Tensor[(600, 32, 32), float16]) { + %0 = fn(%y_0_i0: Tensor[(1600, 768), float16], %y_0_i1: Tensor[(2304, 768), float16], %y_0_i2: Tensor[(2304), float16], + Inline=1, Compiler="cutlass", global_symbol="tvmgen_default_cutlass_main_0", Primitive=1) -> Tensor[(1600, 2304), float16] { + %4 = fn (%FunctionVar_0_0: Tensor[(1600, 768), float16], %FunctionVar_0_1: Tensor[(2304, 768), float16], %FunctionVar_0_2: Tensor[(2304), float16], + PartitionedFromPattern="nn.dense_add_", Composite="cutlass.dense_bias") -> Tensor[(1600, 2304), float16] { + %5 = nn.dense(%FunctionVar_0_0, %FunctionVar_0_1, units=2304); + add(%5, %FunctionVar_0_2) + }; + %4(%y_0_i0, %y_0_i1, %y_0_i2) + }; + %1 = %0(%x0, meta[relay.Constant][0], meta[relay.Constant][1]); + %2 = fn(%y_3_i0: Tensor[(600, 32, 64), float16], %y_3_i1: Tensor[(600, 32, 64), float16], + Inline=1, Compiler="cublas", global_symbol="tvmgen_default_cublas_main_3", Primitive=1) -> Tensor[(600, 32, 32), float16] { + %6 = fn (%FunctionVar_0_01: Tensor[(600, 32, 64), float16], %FunctionVar_0_11: Tensor[(600, 32, 64), float16], + PartitionedFromPattern="nn.batch_matmul_", Composite="cublas.batch_matmul") -> Tensor[(600, 32, 32), float16] { + nn.batch_matmul(%FunctionVar_0_01, %FunctionVar_0_11, out_dtype="float16", transpose_b=True) + }; + %6(%y_3_i0, %y_3_i1) + }; + %3 = %2(%x3, meta[relay.Constant][2]); + (%1, %3) + } + """, + "from_string", + None, + metatable, + ) + + +def expected_outlined_mod(): + return tvm.parser.parse( + """ + #[version = "0.0.5"] + def @main(%x0 : Tensor[(1600, 768), float16], %x3 : Tensor[(600, 32, 64), float16]) -> (Tensor[(1600, 2304), float16], Tensor[(600, 32, 32), float16]) { + %1 = @tvmgen_default_cutlass_main_0(%x0, meta[relay.Constant][0], meta[relay.Constant][1]); + %2 = fn(%y_3_i0: Tensor[(600, 32, 64), float16], %y_3_i1: Tensor[(600, 32, 64), float16], + Inline=1, Compiler="cublas", global_symbol="tvmgen_default_cublas_main_3", Primitive=1) -> Tensor[(600, 32, 32), float16] { + %6 = fn (%FunctionVar_0_01: Tensor[(600, 32, 64), float16], %FunctionVar_0_11: Tensor[(600, 32, 64), float16], + PartitionedFromPattern="nn.batch_matmul_", Composite="cublas.batch_matmul") -> Tensor[(600, 32, 32), float16] { + nn.batch_matmul(%FunctionVar_0_01, %FunctionVar_0_11, out_dtype="float16", transpose_b=True) + }; + %6(%y_3_i0, %y_3_i1) + }; + %3 = %2(%x3, meta[relay.Constant][2]); + (%1, %3) + } + + def @tvmgen_default_cutlass_main_0(%y_0_i0: Tensor[(1600, 768), float16], %y_0_i1: Tensor[(2304, 768), float16], %y_0_i2: Tensor[(2304), float16], + Inline=1, Compiler="cutlass", global_symbol="tvmgen_default_cutlass_main_0", Primitive=1) -> Tensor[(1600, 2304), float16] { + %4 = fn (%FunctionVar_0_0: Tensor[(1600, 768), float16], %FunctionVar_0_1: Tensor[(2304, 768), float16], %FunctionVar_0_2: Tensor[(2304), float16], + PartitionedFromPattern="nn.dense_add_", Composite="cutlass.dense_bias") -> Tensor[(1600, 2304), float16] { + %5 = nn.dense(%FunctionVar_0_0, %FunctionVar_0_1, units=2304); + add(%5, %FunctionVar_0_2) + }; + %4(%y_0_i0, %y_0_i1, %y_0_i2) + } + """, + "from_string", + None, + metatable, + ) + + +def expected_extern_mod(): + return tvm.parser.parse( + """ + #[version = "0.0.5"] + def @main(%x0 : Tensor[(1600, 768), float16], %x3 : Tensor[(600, 32, 64), float16]) -> (Tensor[(1600, 2304), float16], Tensor[(600, 32, 32), float16]) { + %1 = call_lowered(@tvmgen_default_cutlass_main_0, (%x0, meta[relay.Constant][0], meta[relay.Constant][1]), metadata=meta[attributes][0]); + %2 = fn(%y_3_i0: Tensor[(600, 32, 64), float16], %y_3_i1: Tensor[(600, 32, 64), float16], + Inline=1, Compiler="cublas", global_symbol="tvmgen_default_cublas_main_3", Primitive=1) -> Tensor[(600, 32, 32), float16] { + %6 = fn (%FunctionVar_0_01: Tensor[(600, 32, 64), float16], %FunctionVar_0_11: Tensor[(600, 32, 64), float16], + PartitionedFromPattern="nn.batch_matmul_", Composite="cublas.batch_matmul") -> Tensor[(600, 32, 32), float16] { + nn.batch_matmul(%FunctionVar_0_01, %FunctionVar_0_11, out_dtype="float16", transpose_b=True) + }; + %6(%y_3_i0, %y_3_i1) + }; + %3 = %2(%x3, meta[relay.Constant][2]); + (%1, %3) + } + + def @tvmgen_default_cutlass_main_0(%y_0_i0: Tensor[(1600, 768), float16], %y_0_i1: Tensor[(2304, 768), float16], %y_0_i2: Tensor[(2304), float16], + Extern=1) -> Tensor[(1600, 2304), float16] { + %4 = fn (%FunctionVar_0_0: Tensor[(1600, 768), float16], %FunctionVar_0_1: Tensor[(2304, 768), float16], %FunctionVar_0_2: Tensor[(2304), float16], + PartitionedFromPattern="nn.dense_add_", Composite="cutlass.dense_bias") -> Tensor[(1600, 2304), float16] { + %5 = nn.dense(%FunctionVar_0_0, %FunctionVar_0_1, units=2304); + add(%5, %FunctionVar_0_2) + }; + %4(%y_0_i0, %y_0_i1, %y_0_i2) + } + """, + "from_string", + None, + metatable, + ) + + +def test_outline_compiler_functions_with_existing_global_symbols(): + actual_outlined_mod = tvm.relay.transform.OutlineCompilerFunctionsWithExistingGlobalSymbols( + "cutlass" + )(inlined_mod()) + tvm.ir.assert_structural_equal(actual_outlined_mod, expected_outlined_mod(), map_free_vars=True) + + +def test_mark_compiler_functions_as_extern(): + actual_extern_mod = tvm.relay.transform.MarkCompilerFunctionsAsExtern("cutlass")( Review Comment: These won't be used by collage directly. Instead, the custom pass for CUTLASS will use them to avoid boilerplate (and other external codegens can follow the same pattern if they want to also switch to IRModule-at-a-time compilation): ``` transform::Pass CompileForCutlass() { return transform::Sequential( {transforms::OutlineCompilerFunctionsWithExistingGlobalSymbols("cutlass"), CompileForCutlassImpl(), transforms::MarkCompilerFunctionsAsExtern("cutlass")}); } ``` Collage benefits from this because it by making the above switch CUTLASS is fully self contained within the one custom compilation hook and no other special handling is needed by collage or regular cutlass users. - no need for special post-partitioning build - no need for passing compilation options manually - maximizes sharing, of structurally the same partitions, of any boilerplate, and for any tuning -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. 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